Code
# Import Libraries
from nbdev.showdoc import *
import pandas as pd
import numpy as np
from pandas_profiling import ProfileReport
import warnings
warnings.filterwarnings('ignore')
# Load Dataset
df=pd.read_csv('Finance Data/train.csv')Credit Score Data
Dataset contain customer’s credit related information.
Data Dictionary
There are 28 columns discribing customer information Columns:-
ID: Represents a unique identification of an entry
Customer ID: Represents a unique identification of a person
Month: Represents the month of the year
Name: Represents the name of a person
Age: Represents the age of the person
SSN: Represents the social security number of a person
Occupation: Represents the occupation of the person
Annual_Income: Represents the annual income of the person
Monthly_Inhand_Salary: Represents the monthly base salary of a person
Num_Bank_Accounts: Represents the number of bank accounts a person holds
Num_Credit_Card: Represents the number of other credit cards held by a person
Interest_Rate: Represents the interest rate on credit card
Num_of_Loan: Represents the number of loans taken from the bank
Type_of_Loan: Represents the types of loan taken by a person
Delay_from_due_date: Represents the average number of days delayed from the payment date
Num_of_Delayed_Payment: Represents the average number of payments delayed by a person
Changed_Credit_Limit: Represents the percentage change in credit card limit
Num_Credit_Inquiries: Represents the number of credit card inquiries
Credit_Mix: Represents the classification of the mix of credits
Outstanding_Debt: Represents the remaining debt to be paid (in USD)
Credit_Utilization_Ratio: Represents the utilization ratio of credit card
Credit_History_Age: Represents the age of credit history of the person
Payment_of_Min_Amount: Represents whether only the minimum amount was paid by the person
Total_EMI_per_month: Represents the Equated Monthly Installments payments (in USD)
Amount_invested_monthly: Represents the monthly amount invested by the customer (in USD)
Payment_Behaviour: Represents the payment behavior of the customer (in USD)
Monthly_Balance: Represents the monthly balance amount of the customer (in USD)
Credit_Score: Represents the bracket of credit score (Poor, Standard, Good)
Sample Data
Code
df.head().T| 0 | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| ID | 0x1602 | 0x1603 | 0x1604 | 0x1605 | 0x1606 |
| Customer_ID | CUS_0xd40 | CUS_0xd40 | CUS_0xd40 | CUS_0xd40 | CUS_0xd40 |
| Month | January | February | March | April | May |
| Name | Aaron Maashoh | Aaron Maashoh | Aaron Maashoh | Aaron Maashoh | Aaron Maashoh |
| Age | 23 | 23 | -500 | 23 | 23 |
| SSN | 821-00-0265 | 821-00-0265 | 821-00-0265 | 821-00-0265 | 821-00-0265 |
| Occupation | Scientist | Scientist | Scientist | Scientist | Scientist |
| Annual_Income | 19114.12 | 19114.12 | 19114.12 | 19114.12 | 19114.12 |
| Monthly_Inhand_Salary | 1824.843333 | NaN | NaN | NaN | 1824.843333 |
| Num_Bank_Accounts | 3 | 3 | 3 | 3 | 3 |
| Num_Credit_Card | 4 | 4 | 4 | 4 | 4 |
| Interest_Rate | 3 | 3 | 3 | 3 | 3 |
| Num_of_Loan | 4 | 4 | 4 | 4 | 4 |
| Type_of_Loan | Auto Loan, Credit-Builder Loan, Personal Loan,... | Auto Loan, Credit-Builder Loan, Personal Loan,... | Auto Loan, Credit-Builder Loan, Personal Loan,... | Auto Loan, Credit-Builder Loan, Personal Loan,... | Auto Loan, Credit-Builder Loan, Personal Loan,... |
| Delay_from_due_date | 3 | -1 | 3 | 5 | 6 |
| Num_of_Delayed_Payment | 7 | NaN | 7 | 4 | NaN |
| Changed_Credit_Limit | 11.27 | 11.27 | _ | 6.27 | 11.27 |
| Num_Credit_Inquiries | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 |
| Credit_Mix | _ | Good | Good | Good | Good |
| Outstanding_Debt | 809.98 | 809.98 | 809.98 | 809.98 | 809.98 |
| Credit_Utilization_Ratio | 26.82262 | 31.94496 | 28.609352 | 31.377862 | 24.797347 |
| Credit_History_Age | 22 Years and 1 Months | NaN | 22 Years and 3 Months | 22 Years and 4 Months | 22 Years and 5 Months |
| Payment_of_Min_Amount | No | No | No | No | No |
| Total_EMI_per_month | 49.574949 | 49.574949 | 49.574949 | 49.574949 | 49.574949 |
| Amount_invested_monthly | 80.41529543900253 | 118.28022162236736 | 81.699521264648 | 199.4580743910713 | 41.420153086217326 |
| Payment_Behaviour | High_spent_Small_value_payments | Low_spent_Large_value_payments | Low_spent_Medium_value_payments | Low_spent_Small_value_payments | High_spent_Medium_value_payments |
| Monthly_Balance | 312.49408867943663 | 284.62916249607184 | 331.2098628537912 | 223.45130972736786 | 341.48923103222177 |
| Credit_Score | Good | Good | Good | Good | Good |
Code
profile = ProfileReport(df)Code
profile.to_notebook_iframe()